34e74ca2c5
Add {{neighborhood}} placeholder for agent prompts: full seed node
content + ranked neighbors (score = link_strength * node_weight) with
smooth cutoff, minimum 10, cap 25, plus cross-links between included
neighbors.
Rewrite organize.agent prompt to focus on structural graph work:
merging duplicates, superseding junk, calibrating weights, creating
concept hubs.
Add weight-set CLI command for direct node weight manipulation.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
466 lines
17 KiB
Rust
466 lines
17 KiB
Rust
// Agent definitions: self-contained files with query + prompt template.
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//
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// Each agent is a file in the agents/ directory:
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// - First line: JSON header (agent, query, model, schedule)
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// - After blank line: prompt template with {{placeholder}} lookups
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//
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// Placeholders are resolved at runtime:
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// {{topology}} — graph topology header
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// {{nodes}} — query results formatted as node sections
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// {{episodes}} — alias for {{nodes}}
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// {{health}} — graph health report
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// {{pairs}} — interference pairs from detect_interference
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// {{rename}} — rename candidates
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// {{split}} — split detail for the first query result
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//
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// The query selects what to operate on; placeholders pull in context.
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use crate::graph::Graph;
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use crate::neuro::{consolidation_priority, ReplayItem};
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use crate::search;
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use crate::store::Store;
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use serde::Deserialize;
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use std::path::PathBuf;
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/// Agent definition: config (from JSON header) + prompt (raw markdown body).
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#[derive(Clone, Debug)]
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pub struct AgentDef {
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pub agent: String,
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pub query: String,
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pub prompt: String,
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pub model: String,
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pub schedule: String,
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pub tools: Vec<String>,
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}
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/// The JSON header portion (first line of the file).
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#[derive(Deserialize)]
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struct AgentHeader {
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agent: String,
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#[serde(default)]
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query: String,
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#[serde(default = "default_model")]
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model: String,
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#[serde(default)]
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schedule: String,
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#[serde(default)]
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tools: Vec<String>,
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}
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fn default_model() -> String { "sonnet".into() }
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/// Parse an agent file: first line is JSON config, rest is the prompt.
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fn parse_agent_file(content: &str) -> Option<AgentDef> {
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let (first_line, rest) = content.split_once('\n')?;
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let header: AgentHeader = serde_json::from_str(first_line.trim()).ok()?;
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// Skip optional blank line between header and prompt body
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let prompt = rest.strip_prefix('\n').unwrap_or(rest);
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Some(AgentDef {
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agent: header.agent,
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query: header.query,
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prompt: prompt.to_string(),
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model: header.model,
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schedule: header.schedule,
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tools: header.tools,
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})
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}
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fn agents_dir() -> PathBuf {
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let repo = PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("agents");
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if repo.is_dir() { return repo; }
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crate::store::memory_dir().join("agents")
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}
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/// Load all agent definitions.
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pub fn load_defs() -> Vec<AgentDef> {
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let dir = agents_dir();
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let Ok(entries) = std::fs::read_dir(&dir) else { return Vec::new() };
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entries
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.filter_map(|e| e.ok())
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.filter(|e| {
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let p = e.path();
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p.extension().map(|x| x == "agent" || x == "md").unwrap_or(false)
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})
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.filter_map(|e| {
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let content = std::fs::read_to_string(e.path()).ok()?;
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parse_agent_file(&content)
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})
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.collect()
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}
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/// Look up a single agent definition by name.
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pub fn get_def(name: &str) -> Option<AgentDef> {
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let dir = agents_dir();
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for ext in ["agent", "md"] {
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let path = dir.join(format!("{}.{}", name, ext));
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if let Ok(content) = std::fs::read_to_string(&path) {
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if let Some(def) = parse_agent_file(&content) {
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return Some(def);
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}
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}
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}
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load_defs().into_iter().find(|d| d.agent == name)
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}
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/// Result of resolving a placeholder: text + any affected node keys.
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struct Resolved {
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text: String,
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keys: Vec<String>,
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}
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/// Resolve a single {{placeholder}} by name.
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/// Returns the replacement text and any node keys it produced (for visit tracking).
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fn resolve(
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name: &str,
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store: &Store,
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graph: &Graph,
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keys: &[String],
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count: usize,
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) -> Option<Resolved> {
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match name {
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"topology" => Some(Resolved {
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text: super::prompts::format_topology_header(graph),
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keys: vec![],
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}),
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"nodes" | "episodes" => {
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let items = keys_to_replay_items(store, keys, graph);
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Some(Resolved {
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text: super::prompts::format_nodes_section(store, &items, graph),
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keys: vec![], // keys already tracked from query
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})
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}
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"health" => Some(Resolved {
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text: super::prompts::format_health_section(store, graph),
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keys: vec![],
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}),
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"pairs" => {
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let mut pairs = crate::neuro::detect_interference(store, graph, 0.5);
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pairs.truncate(count);
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let pair_keys: Vec<String> = pairs.iter()
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.flat_map(|(a, b, _)| vec![a.clone(), b.clone()])
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.collect();
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Some(Resolved {
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text: super::prompts::format_pairs_section(&pairs, store, graph),
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keys: pair_keys,
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})
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}
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"rename" => {
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let (rename_keys, section) = super::prompts::format_rename_candidates(store, count);
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Some(Resolved { text: section, keys: rename_keys })
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}
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"split" => {
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let key = keys.first()?;
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Some(Resolved {
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text: super::prompts::format_split_plan_node(store, graph, key),
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keys: vec![], // key already tracked from query
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})
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}
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"organize" => {
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// Show seed nodes with their neighbors for exploratory organizing
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use crate::store::NodeType;
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// Helper: shell-quote keys containing #
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let sq = |k: &str| -> String {
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if k.contains('#') { format!("'{}'", k) } else { k.to_string() }
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};
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let mut text = format!("### Seed nodes ({} starting points)\n\n", keys.len());
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let mut result_keys = Vec::new();
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for key in keys {
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let Some(node) = store.nodes.get(key) else { continue };
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if node.deleted { continue; }
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let is_journal = node.node_type == NodeType::EpisodicSession;
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let tag = if is_journal { " [JOURNAL — no delete]" } else { "" };
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let words = node.content.split_whitespace().count();
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text.push_str(&format!("#### {}{} ({} words)\n\n", sq(key), tag, words));
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// Show first ~200 words of content as preview
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let preview: String = node.content.split_whitespace()
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.take(200).collect::<Vec<_>>().join(" ");
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if words > 200 {
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text.push_str(&format!("{}...\n\n", preview));
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} else {
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text.push_str(&format!("{}\n\n", node.content));
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}
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// Show neighbors with strengths
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let neighbors = graph.neighbors(key);
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if !neighbors.is_empty() {
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text.push_str("**Neighbors:**\n");
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for (nbr, strength) in neighbors.iter().take(15) {
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let nbr_type = store.nodes.get(nbr.as_str())
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.map(|n| match n.node_type {
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NodeType::EpisodicSession => " [journal]",
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NodeType::EpisodicDaily => " [daily]",
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_ => "",
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})
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.unwrap_or("");
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text.push_str(&format!(" [{:.1}] {}{}\n", strength, sq(nbr), nbr_type));
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}
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if neighbors.len() > 15 {
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text.push_str(&format!(" ... and {} more\n", neighbors.len() - 15));
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}
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text.push('\n');
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}
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text.push_str("---\n\n");
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result_keys.push(key.clone());
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}
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text.push_str("Use `poc-memory render KEY` and `poc-memory query \"neighbors('KEY')\"` to explore further.\n");
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Some(Resolved { text, keys: result_keys })
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}
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"conversations" => {
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let fragments = super::knowledge::select_conversation_fragments(count);
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let fragment_ids: Vec<String> = fragments.iter()
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.map(|(id, _)| id.clone())
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.collect();
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let text = fragments.iter()
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.map(|(id, text)| format!("### Session {}\n\n{}", id, text))
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.collect::<Vec<_>>()
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.join("\n\n---\n\n");
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Some(Resolved { text, keys: fragment_ids })
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}
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"siblings" | "neighborhood" => {
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let mut out = String::new();
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let mut all_keys: Vec<String> = Vec::new();
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const MAX_NEIGHBORS: usize = 25;
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for key in keys {
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let Some(node) = store.nodes.get(key.as_str()) else { continue };
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let neighbors = graph.neighbors(key);
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// Seed node with full content
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out.push_str(&format!("## {} (seed)\n\n{}\n\n", key, node.content));
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all_keys.push(key.clone());
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// Rank neighbors by link_strength * node_weight
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// Include all if <= 10, otherwise take top MAX_NEIGHBORS
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let mut ranked: Vec<(String, f32, f32)> = neighbors.iter()
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.filter_map(|(nbr, strength)| {
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store.nodes.get(nbr.as_str()).map(|n| {
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let node_weight = n.weight.max(0.01);
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let score = strength * node_weight;
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(nbr.to_string(), *strength, score)
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})
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})
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.collect();
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ranked.sort_by(|a, b| b.2.total_cmp(&a.2));
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let total = ranked.len();
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let included: Vec<_> = if total <= 10 {
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ranked
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} else {
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// Smooth cutoff: threshold scales with neighborhood size
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// Generous — err on including too much so the agent can
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// see and clean up junk. 20 → top 75%, 50 → top 30%
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let top_score = ranked.first().map(|(_, _, s)| *s).unwrap_or(0.0);
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let ratio = (15.0 / total as f32).min(1.0);
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let threshold = top_score * ratio;
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ranked.into_iter()
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.enumerate()
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.take_while(|(i, (_, _, score))| *i < 10 || *score >= threshold)
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.take(MAX_NEIGHBORS)
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.map(|(_, item)| item)
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.collect()
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};
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if !included.is_empty() {
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if total > included.len() {
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out.push_str(&format!("### Neighbors (top {} of {}, ranked by importance)\n\n",
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included.len(), total));
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} else {
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out.push_str("### Neighbors\n\n");
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}
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let included_keys: std::collections::HashSet<&str> = included.iter()
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.map(|(k, _, _)| k.as_str()).collect();
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for (nbr, strength, _score) in &included {
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if let Some(n) = store.nodes.get(nbr.as_str()) {
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out.push_str(&format!("#### {} (link: {:.2})\n\n{}\n\n",
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nbr, strength, n.content));
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all_keys.push(nbr.to_string());
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}
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}
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// Cross-links between included neighbors
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let mut cross_links = Vec::new();
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for (nbr, _, _) in &included {
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for (nbr2, strength) in graph.neighbors(nbr) {
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if nbr2.as_str() != key
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&& included_keys.contains(nbr2.as_str())
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&& nbr.as_str() < nbr2.as_str()
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{
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cross_links.push((nbr.clone(), nbr2, strength));
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}
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}
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}
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if !cross_links.is_empty() {
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out.push_str("### Cross-links between neighbors\n\n");
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for (a, b, s) in &cross_links {
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out.push_str(&format!(" {} ↔ {} ({:.2})\n", a, b, s));
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}
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out.push_str("\n");
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}
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}
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}
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Some(Resolved { text: out, keys: all_keys })
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}
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// targets/context: aliases for challenger-style presentation
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"targets" => {
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let items = keys_to_replay_items(store, keys, graph);
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Some(Resolved {
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text: super::prompts::format_nodes_section(store, &items, graph),
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keys: vec![],
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})
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}
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"hubs" => {
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// Top hub nodes by degree, spread apart (skip neighbors of already-selected hubs)
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let mut hubs: Vec<(String, usize)> = store.nodes.iter()
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.filter(|(k, n)| !n.deleted && !k.starts_with('_'))
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.map(|(k, _)| {
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let degree = graph.neighbors(k).len();
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(k.clone(), degree)
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})
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.collect();
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hubs.sort_by(|a, b| b.1.cmp(&a.1));
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let mut selected = Vec::new();
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let mut seen: std::collections::HashSet<String> = std::collections::HashSet::new();
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for (key, degree) in &hubs {
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if seen.contains(key) { continue; }
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selected.push(format!(" - {} (degree {})", key, degree));
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// Mark neighbors as seen so we pick far-apart hubs
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for (nbr, _) in graph.neighbors(key) {
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seen.insert(nbr.clone());
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}
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seen.insert(key.clone());
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if selected.len() >= 20 { break; }
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}
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let text = format!("## Hub nodes (link targets)\n\n{}", selected.join("\n"));
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Some(Resolved { text, keys: vec![] })
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}
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// node:KEY — inline a node's content by key
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other if other.starts_with("node:") => {
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let key = &other[5..];
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store.nodes.get(key).map(|n| Resolved {
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text: n.content.clone(),
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keys: vec![key.to_string()],
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})
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}
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_ => None,
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}
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}
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/// Resolve all {{placeholder}} patterns in a prompt template.
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/// Returns the resolved text and all node keys collected from placeholders.
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pub fn resolve_placeholders(
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template: &str,
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store: &Store,
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graph: &Graph,
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keys: &[String],
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count: usize,
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) -> (String, Vec<String>) {
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let mut result = template.to_string();
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let mut extra_keys = Vec::new();
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loop {
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let Some(start) = result.find("{{") else { break };
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let Some(end) = result[start + 2..].find("}}") else { break };
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let end = start + 2 + end;
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let name = result[start + 2..end].trim().to_lowercase();
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match resolve(&name, store, graph, keys, count) {
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Some(resolved) => {
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extra_keys.extend(resolved.keys);
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result.replace_range(start..end + 2, &resolved.text);
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}
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None => {
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let msg = format!("(unknown: {})", name);
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result.replace_range(start..end + 2, &msg);
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}
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}
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}
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(result, extra_keys)
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}
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/// Run a config-driven agent: query → resolve placeholders → prompt.
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pub fn run_agent(
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store: &Store,
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def: &AgentDef,
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count: usize,
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) -> Result<super::prompts::AgentBatch, String> {
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let graph = store.build_graph();
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// Run the query if present
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let keys = if !def.query.is_empty() {
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let mut stages = search::Stage::parse_pipeline(&def.query)?;
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let has_limit = stages.iter().any(|s|
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matches!(s, search::Stage::Transform(search::Transform::Limit(_))));
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if !has_limit {
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stages.push(search::Stage::Transform(search::Transform::Limit(count)));
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}
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let results = search::run_query(&stages, vec![], &graph, store, false, count);
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if results.is_empty() {
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return Err(format!("{}: query returned no results", def.agent));
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}
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results.into_iter().map(|(k, _)| k).collect::<Vec<_>>()
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} else {
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vec![]
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};
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let (prompt, extra_keys) = resolve_placeholders(&def.prompt, store, &graph, &keys, count);
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// Identity and instructions are now pulled in via {{node:KEY}} placeholders.
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// Agents should include {{node:core-personality}} and {{node:memory-instructions-core}}
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// in their prompt templates. The resolve_placeholders call below handles this.
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// Merge query keys with any keys produced by placeholder resolution
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let mut all_keys = keys;
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all_keys.extend(extra_keys);
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Ok(super::prompts::AgentBatch { prompt, node_keys: all_keys })
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}
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/// Convert a list of keys to ReplayItems with priority and graph metrics.
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pub fn keys_to_replay_items(
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store: &Store,
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keys: &[String],
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graph: &Graph,
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) -> Vec<ReplayItem> {
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keys.iter()
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.filter_map(|key| {
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let node = store.nodes.get(key)?;
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let priority = consolidation_priority(store, key, graph, None);
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let cc = graph.clustering_coefficient(key);
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Some(ReplayItem {
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key: key.clone(),
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priority,
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interval_days: node.spaced_repetition_interval,
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emotion: node.emotion,
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cc,
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classification: "unknown",
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outlier_score: 0.0,
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})
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})
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.collect()
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}
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